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Record W4385406196 · doi:10.1089/jamp.2023.29087.rbt

Magnetic Resonance Imaging of Aerosol Deposition

2023· article· en· W4385406196 on OpenAlexaff
Richard B. Thompson, Chantal Darquenne

Bibliographic record

VenueJournal of Aerosol Medicine and Pulmonary Drug Delivery · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAtomic and Subatomic Physics Research
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAerosolDeposition (geology)Magnetic resonance imagingNuclear magnetic resonanceMaterials scienceChemistryPhysicsRadiologyMedicineBiology

Abstract

fetched live from OpenAlex

Nuclear magnetic resonance imaging (MRI) uses non-ionizing radiation and offers a host of contrast mechanisms with the potential to quantify aerosol deposition. This chapter introduces the physics of MRI, its use in lung imaging, and more specifically, the methods that are used for the detection of regional distributions of inhaled particles. The most common implementation of MRI is based on imaging of hydrogen atoms ( 1 H) in water. The regional deposition of aerosol particles can be measured by the perturbation of the acquired 1 H signals via labeling of the aerosol with contrast agents. Existing in vitro human and in vivo animal model measurements of regional aerosol deposition in the respiratory tract are described, demonstrating the capability of MRI to assess aerosol deposition in the lung.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.682
Threshold uncertainty score0.488

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.014
GPT teacher head0.265
Teacher spread0.251 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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